
# Load libraries
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)

import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from pylab import rcParams
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 72

import seaborn as sns
sns.set_style("darkgrid")
sns.set_context(context="paper", font_scale=1.5, rc=None)
sns.set(font="serif")

#import plotly.express as px
#import plotly.graph_objects as go

import geopandas as gpd

from sklearn import preprocessing
from sklearn.preprocessing import OrdinalEncoder


import libpysal as ps
from libpysal  import weights
from libpysal.weights import Queen

import esda
from esda.moran import Moran, Moran_Local

import splot
from splot.esda import moran_scatterplot, plot_moran, lisa_cluster, plot_local_autocorrelation
from splot.libpysal import plot_spatial_weights

from giddy.directional import Rose

import statsmodels.api as sm
import statsmodels.formula.api as smf
#from stargazer.stargazer import Stargazer, LineLocation

from spreg import OLS
from spreg import MoranRes
from spreg import ML_Lag
from spreg import ML_Error 

from mgwr.gwr import GWR, MGWR
from mgwr.sel_bw import Sel_BW
from mgwr.utils import shift_colormap, truncate_colormap

import warnings
warnings.filterwarnings('ignore') 
import time


# Load  dataset 
gdf  = gpd.read_file('data/nb_df.gpkg')

#Filtering bools
bools= gdf.select_dtypes(include=['bool'])

enc = OrdinalEncoder()
bools_encoded = enc.fit_transform(bools)
bools_encoded = pd.DataFrame(bools_encoded, columns = bools.columns)

#removing old bools
gdf = gdf.drop(bools.columns, axis = 1)
gdf = gdf.reset_index(drop = True)

#Adding encoded bools
gpd.GeoDataFrame(pd.concat([gdf, bools_encoded], axis = 1))


fig, ax = plt.subplots(figsize=(6, 6))
gdf.plot(color = 'white', edgecolor = 'black', ax = ax, linewidth = .5 )
#gdf.centroid.plot(ax = ax)
ax.set_title('Map of neighbourhoods in CDMX', fontsize=12)
ax.axis("off")
plt.show()
gdf.explore()


fig, ax = plt.subplots(figsize=(6, 6))
gdf.plot(column='price_small', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)
ax.set_title('Prices of houses by neighbourhood in CDMX (MM MXN)', fontsize=12)
ax.axis("off")
plt.show()


y = gdf['price_small'].values.reshape((-1,1)) # reshape is needed to have column array
y.shape

(699, 1)


X_df =gdf.drop(['id_right', 'age', 'price_small', 'construction_size', 'geometry', 'price_per_area_small', 'latitude', 'longitude', 'price', 'price_per_area'], axis = 1)
X = X_df.values

min_max_scaler = preprocessing.MinMaxScaler()
X= min_max_scaler.fit_transform(X)

X_df


gdf["centroids"] = gdf["geometry"].centroid
u =  gdf["centroids"].x
v =  gdf["centroids"].y
coords = list(zip(u,v))


gdf.centroids.x

0      2.793472e+06
1      2.794483e+06
2      2.792847e+06
3      2.801276e+06
4      2.793442e+06
           ...     
694    2.798141e+06
695    2.800677e+06
696    2.801099e+06
697    2.795124e+06
698    2.792056e+06
Length: 699, dtype: float64


#Generating spatial weight matrix
X_df_loc =gdf.drop(['price_small', 'price_per_area_small', 'latitude', 'longitude', 'price', 'price_per_area'], axis = 1).copy()
display(X_df_loc)
# Generate W from the GeoDataFrame
w = weights.distance.KNN.from_dataframe(X_df_loc, k=8)
# Row-standardization
w.transform = "R"
pd.DataFrame(w)


from splot import esda as esdaplot
#Calculating spatial lag of variables of interest
ban = ["geometry", "centroids", "id_right"]
cols = [col for col in X_df_loc.columns if col not in ban ]

for var in cols:
    X_df_loc["w_"+ var] = weights.lag_spatial(w, (X_df_loc[var]))

for var in cols:  
    try:
        print("Variable: ", var)
        #Local Moran’s 
        lisa = esda.moran.Moran_Local(X_df_loc["w_"+var], w)

        #plot
        # Set up figure and axes
        f, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 12))


        # Make the axes accessible with single indexing
        axs = axs.flatten()

        # Subplot 1 #
        # Choropleth of local statistics
        # Grab first axis in the figure
        ax = axs[0]

        # Assign new column with local statistics on-the-fly
        X_df_loc.assign(
            Is=lisa.Is
            # Plot choropleth of local statistics
        ).plot(
            column="Is",
            cmap="coolwarm",
            scheme="quantiles",
            k=5,
            edgecolor="white",
            linewidth=0.1,
            alpha=0.75,
            legend=True,
            ax=ax,
        )

        # Subplot 2 #
        # Quadrant categories
        # Grab second axis of local statistics
        ax = axs[1]
        # Plot Quadrant colors (note to ensure all polygons are assigned a
        # quadrant, we "trick" the function by setting significance level to
        # 1 so all observations are treated as "significant" and thus assigned
        # a quadrant color
        esdaplot.lisa_cluster(lisa, X_df_loc, p=1, ax=ax)

        # Subplot 3 #
        # Significance map
        # Grab third axis of local statistics
        ax = axs[2]
        #
        # Find out significant observations
        labels = pd.Series(
            1 * (lisa.p_sim < 0.05),  # Assign 1 if significant, 0 otherwise
            index=X_df_loc.index  # Use the index in the original data
            # Recode 1 to "Significant and 0 to "Non-significant"
        ).map({1: "Significant", 0: "Non-Significant"})
        # Assign labels to `db` on the fly
        X_df_loc.assign(
            cl=labels
            # Plot choropleth of (non-)significant areas
        ).plot(
            column="cl",
            categorical=True,
            k=2,
            cmap="coolwarm",
            linewidth=0.1,
            edgecolor="white",
            legend=True,
            ax=ax,
        )


        # Subplot 4 #
        # Cluster map
        # Grab second axis of local statistics
        ax = axs[3]
        # Plot Quadrant colors In this case, we use a 5% significance
        # level to select polygons as part of statistically significant
        # clusters
        esdaplot.lisa_cluster(lisa, X_df_loc, p=0.05, ax=ax)

        # Figure styling #
        # Set title to each subplot
        for i, ax in enumerate(axs.flatten()):
            ax.set_axis_off()
            ax.set_title(
                [
                    "Local Statistics of " + var,
                    "Scatterplot Quadrant of " + var,
                    "Statistical Significance of "+ var,
                    "Moran Cluster Map of "+ var,
                ][i],
                y=0,
            )
        # Tight layout to minimize in-between white space


        # Display the figure
        plt.show()
    except: 
        print("Corrupt variable")

['bedrooms', 'bathrooms', 'half_bathrooms', 'lot_size', 'construction_size', 'age', 'is_house', 'real_age', 'balcony', 'water strorage', 'air_conditioning', 'pool', 'furniture', 'accasible_for_handicap', 'first_floor', 'security', 'parking_space', 'kitchen', 'garden', 'roof_Garden', 'service_room', 'gated_community', 'high_impact/area', 'low_impact/area', 'parks', 'schools', 'university', 'hospital', 'sport_facility', 'supermarket', 'mall', 'stadium', 'historic', 'museum', 'airport', 'industry', 'subway', 'bus', 'w_bedrooms', 'w_bathrooms', 'w_half_bathrooms', 'w_lot_size', 'w_construction_size', 'w_age', 'w_is_house', 'w_real_age', 'w_balcony', 'w_water strorage', 'w_air_conditioning', 'w_pool', 'w_furniture', 'w_accasible_for_handicap', 'w_first_floor', 'w_security', 'w_parking_space', 'w_kitchen', 'w_garden', 'w_roof_Garden', 'w_service_room', 'w_gated_community', 'w_high_impact/area', 'w_low_impact/area', 'w_parks', 'w_schools', 'w_university', 'w_hospital', 'w_sport_facility', 'w_supermarket', 'w_mall', 'w_stadium', 'w_historic', 'w_museum', 'w_airport', 'w_industry', 'w_subway', 'w_bus']
Variable:  bedrooms

Variable:  bathrooms

Variable:  half_bathrooms

Variable:  lot_size

Variable:  construction_size

Variable:  age

Variable:  is_house

Variable:  real_age

Variable:  balcony

Variable:  water strorage


%%time
gwr_selector = Sel_BW(coords, y, X, kernel="gaussian")
gwr_bw = gwr_selector.search(criterion='AIC')

CPU times: user 23.3 s, sys: 601 ms, total: 23.9 s
Wall time: 6.58 s


print('GWR bandwidth =', gwr_bw)

GWR bandwidth = 115.0


gwr_results = GWR(coords, y, X, gwr_bw, kernel='gaussian').fit()
gwr_results.summary()

===========================================================================
Model type                                                         Gaussian
Number of observations:                                                 699
Number of covariates:                                                    37

Global Regression Results
---------------------------------------------------------------------------
Residual sum of squares:                                           4451.154
Log-likelihood:                                                   -1638.856
AIC:                                                               3351.712
AICc:                                                              3358.203
BIC:                                                                115.285
R2:                                                                   0.605
Adj. R2:                                                              0.583

Variable                              Est.         SE  t(Est/SE)    p-value
------------------------------- ---------- ---------- ---------- ----------
X0                                   5.932      0.888      6.682      0.000
X1                                  -4.292      1.303     -3.295      0.001
X2                                  11.508      1.327      8.672      0.000
X3                                  11.263      1.361      8.276      0.000
X4                                  14.579      1.553      9.386      0.000
X5                                   4.428      1.301      3.404      0.001
X6                                  -0.154      0.754     -0.205      0.838
X7                                   0.713      0.959      0.743      0.457
X8                                  -0.882      0.754     -1.171      0.242
X9                                   4.053      1.867      2.171      0.030
X10                                  0.800      1.135      0.704      0.481
X11                                 -0.161      2.564     -0.063      0.950
X12                                 -0.365      1.858     -0.197      0.844
X13                                  1.553      2.793      0.556      0.578
X14                                 -0.281      1.187     -0.236      0.813
X15                                 -0.172      0.733     -0.235      0.814
X16                                 -0.079      0.392     -0.202      0.840
X17                                  0.015      1.328      0.011      0.991
X18                                  0.979      0.680      1.440      0.150
X19                                 -0.216      0.562     -0.384      0.701
X20                                 -1.204      2.698     -0.446      0.655
X21                                 -4.342      1.389     -3.125      0.002
X22                                  0.887      1.320      0.672      0.501
X23                                 -2.340      0.701     -3.338      0.001
X24                                  1.831      0.815      2.247      0.025
X25                                 -0.883      1.424     -0.620      0.535
X26                                 -0.077      0.948     -0.082      0.935
X27                                 -3.394      0.796     -4.263      0.000
X28                                 -0.574      1.471     -0.390      0.696
X29                                 -2.367      1.439     -1.645      0.100
X30                                  0.011      1.087      0.010      0.992
X31                                 -1.195      1.107     -1.079      0.281
X32                                 -2.961      0.950     -3.116      0.002
X33                                  0.840      1.406      0.597      0.550
X34                                  1.571      1.008      1.558      0.119
X35                                 -2.148      1.648     -1.303      0.192
X36                                 -0.036      0.919     -0.039      0.969

Geographically Weighted Regression (GWR) Results
---------------------------------------------------------------------------
Spatial kernel:                                           Adaptive gaussian
Bandwidth used:                                                     115.000

Diagnostic information
---------------------------------------------------------------------------
Residual sum of squares:                                           3009.668
Effective number of parameters (trace(S)):                          106.657
Degree of freedom (n - trace(S)):                                   592.343
Sigma estimate:                                                       2.254
Log-likelihood:                                                   -1502.085
AIC:                                                               3219.483
AICc:                                                              3259.113
BIC:                                                               3709.283
R2:                                                                   0.733
Adjusted R2:                                                          0.684
Adj. alpha (95%):                                                     0.017
Adj. critical t value (95%):                                          2.385

Summary Statistics For GWR Parameter Estimates
---------------------------------------------------------------------------
Variable                   Mean        STD        Min     Median        Max
-------------------- ---------- ---------- ---------- ---------- ----------
X0                        5.579      2.612      1.156      6.397      9.649
X1                       -3.848      1.704     -6.982     -3.454     -1.332
X2                        9.966      2.520      6.364      9.160     15.848
X3                       10.859      3.226      4.570     10.128     16.492
X4                       26.916      9.663     10.916     27.191     41.999
X5                        5.125      1.713      1.540      5.391      8.224
X6                       -0.915      1.243     -2.523     -1.474      1.761
X7                        0.967      0.539     -0.174      0.883      2.337
X8                       -0.232      0.418     -1.118     -0.188      0.416
X9                        5.527      1.758      2.378      5.413      9.336
X10                       0.239      0.364     -0.428      0.215      1.262
X11                       3.526      4.967     -1.181      1.403     15.497
X12                       0.161      0.889     -1.360      0.093      2.158
X13                       2.681      1.527     -0.253      2.733      5.369
X14                      -0.139      0.523     -1.195     -0.105      0.872
X15                       0.098      0.643     -1.427      0.238      0.937
X16                       0.229      0.627     -0.976      0.284      1.305
X17                       0.172      0.273     -0.449      0.168      0.747
X18                       0.785      0.560     -0.257      0.743      2.132
X19                       0.232      0.619     -0.969      0.459      1.611
X20                       1.536      5.617     -4.490     -0.421     34.436
X21                      -3.696      1.201     -7.673     -3.603     -1.777
X22                      -0.426      2.438     -5.431     -0.728      3.705
X23                      -1.883      1.195     -4.313     -1.711      0.105
X24                       1.194      1.260     -1.372      1.087      3.683
X25                      -0.760      2.617     -4.898     -0.996      4.264
X26                       0.643      0.716     -1.144      0.785      1.853
X27                      -3.923      2.608     -9.602     -3.440     -0.184
X28                      -3.422      2.830    -11.664     -2.552      1.982
X29                      -2.860      2.343     -7.597     -2.897      0.804
X30                      -0.721      2.818     -6.615      0.067      3.385
X31                      -1.045      1.851     -4.513     -1.148      2.803
X32                      -3.189      1.581     -6.953     -3.316      0.248
X33                       1.876      2.466     -2.282      1.373      6.637
X34                       3.950      2.507     -0.680      4.472      7.595
X35                      -4.505      1.950     -8.292     -4.536     -0.584
X36                      -0.889      0.983     -3.256     -0.826      1.143
===========================================================================


# As reference, here is the (average) R2, AIC, and AICc
print('Mean R2 =', gwr_results.R2)
print('AIC =', gwr_results.aic)
print('AICc =', gwr_results.aicc)

Mean R2 = 0.732583390492922
AIC = 3219.483330300514
AICc = 3259.113147749645


#Copy to preserve original gdf
gdf_r = gdf.copy()
# Add R2 to GeoDataframe 
gdf_r['gwr_R2'] = gwr_results.localR2


fig, ax = plt.subplots(figsize=(6, 6))
gdf_r.plot(column='gwr_R2', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)
ax.set_title('Local R2', fontsize=12)
ax.axis("off")
gdf_r.explore()
plt.show()


gdf_r['res'] = gwr_results.resid_response
fig, ax = plt.subplots(figsize=(6, 6))
gdf_r.plot(column='res', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)
ax.set_title('Local Residuals', fontsize=12)
ax.axis("off")
gdf_r.explore()
#plt.savefig('myMap.png',dpi=150, bbox_inches='tight')
plt.show()


#plot distribution
import seaborn as sns
sns.histplot(data=gdf_r, x="res")

<AxesSubplot: xlabel='res', ylabel='Count'>


#Store coef column names

coef_names = []
for num,column in enumerate (X_df.columns):


    gdf_r['gwr_{}'.format(column)] = gwr_results.params[:,num+1]
    
    coef_names.append('gwr_{}'.format(column))
gdf_r.head()


#creating copy()
gdf_abs =gdf_r.copy()
gdf_abs

#acum per coeficient
mean_coef= []
#getting abs of each var
for num,column in enumerate ('gwr_'+X_df.columns):
    gdf_abs[column] = gdf_abs[column].apply(lambda x : abs(x))
    mean_coef.append(gdf_abs[column].mean())
gdf_abs

df = pd.DataFrame(mean_coef).T
df.columns= X_df.columns
df = df.T.reset_index().rename(columns={'index': 'variable', 0: "coef_abs_value"})
df_coefs = df.sort_values(by= "coef_abs_value" )
df_coefs.tail(20)


plt.figure(figsize=(3, 20))
plt.barh(df_coefs["variable"][20:], df_coefs["coef_abs_value"][20:], height=0.2, color='blue', alpha=0.8, align='center', linewidth=0)

<BarContainer object of 16 artists>


# Filter t-values: standard alpha = 0.05
gwr_filtered_t = gwr_results.filter_tvals(alpha = 0.05)
# Filter t-values: corrected alpha due to multiple testing
gwr_filtered_tc = gwr_results.filter_tvals()


for num,col in enumerate (coef_names):
    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18,6))

    gdf_r.plot(column= col, cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=axes[0])

    gdf_r.plot(column= col, cmap = 'coolwarm', linewidth=0.05, scheme = 'FisherJenks', k=5, legend=False, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=axes[1])
    gdf_r[gwr_filtered_t[:,num+1] == 0].plot(color='white', linewidth=0.05, edgecolor='black', ax=axes[1])


    gdf_r.plot(column= col, cmap = 'coolwarm', linewidth=0.05, scheme = 'FisherJenks', k=5, legend=False, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=axes[2])
    gdf_r[gwr_filtered_tc[:,num+1] == 0].plot(color='white', linewidth=0.05, edgecolor='black', ax=axes[2])

    plt.tight_layout()

    axes[0].axis("off")
    axes[1].axis("off")
    axes[2].axis("off")

    axes[0].set_title('(a) '+ col +' (BW: ' + str(gwr_bw) +'), all coeffs', fontsize=12)
    axes[1].set_title('(b) '+ col+' (BW: ' + str(gwr_bw) +'), significant coeffs',     fontsize=12)
    axes[2].set_title('(c) '+col +' (BW: ' + str(gwr_bw) +'), significant coeffs and corr. p-values',     fontsize=12)
    plt.show()


# Monte Carlo test of spatial variability: 500 iterations 
gwr_p_values_stationarity = gwr_results.spatial_variability(gwr_selector, 5)#This number should be more than 100 for the final run


df = pd.DataFrame(gwr_p_values_stationarity[1:]).T
df.columns =  coef_names
df


cols_variation = df.columns[(df < 0.05).any()]
print("cols with spatial variability: ",cols_variation )

cols with spatial variability:  Index(['gwr_bathrooms', 'gwr_half_bathrooms', 'gwr_lot_size', 'gwr_real_age',
       'gwr_furniture', 'gwr_first_floor', 'gwr_kitchen', 'gwr_service_room',
       'gwr_gated_community', 'gwr_high_impact/area', 'gwr_low_impact/area',
       'gwr_parks', 'gwr_university', 'gwr_sport_facility', 'gwr_mall',
       'gwr_stadium', 'gwr_historic', 'gwr_museum', 'gwr_airport',
       'gwr_industry', 'gwr_subway', 'gwr_bus'],
      dtype='object')


LCC, VIF, CN, VDP = gwr_results.local_collinearity()
pd.DataFrame(VIF, columns= coef_names).describe().round(2)


#Columns with local multicolinearity
df = pd.DataFrame(VIF, columns= coef_names).describe().round(2).iloc[1]
print("Columns with local multicolinearity", df[df>10].index)

Columns with local multicolinearity Index(['gwr_subway'], dtype='object')


gdf_r['gwr_CN'] = CN

fig, ax = plt.subplots(figsize=(6, 6))
gdf_r.plot(column='gwr_CN', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)
ax.set_title('Local multicollinearity (CN > 30)?', fontsize=12)
ax.axis("off")
#plt.savefig('myMap.png',dpi=150, bbox_inches='tight')
plt.show()


mgwr_selector = Sel_BW(coords, y, X, multi=True, kernel='gaussian')
mgwr_bw = mgwr_selector.search(criterion='AIC')
mgwr_bw

Backfitting:   0%|          | 0/200 [00:00<?, ?it/s]

array([698., 698.,  43.,  43.,  43.,  43., 696., 698., 698., 566., 698.,
       145., 698., 293., 698., 698., 378., 698., 698., 220., 698., 698.,
       206., 698., 642., 698., 698., 698., 698., 698., 698., 698.,  44.,
       698.,  56., 698., 698.])


mgwr_results = MGWR(coords, y, X, mgwr_selector, kernel='gaussian').fit()
mgwr_results.summary()

Inference:   0%|          | 0/1 [00:00<?, ?it/s]

===========================================================================
Model type                                                         Gaussian
Number of observations:                                                 699
Number of covariates:                                                    37

Global Regression Results
---------------------------------------------------------------------------
Residual sum of squares:                                           4451.154
Log-likelihood:                                                   -1638.856
AIC:                                                               3351.712
AICc:                                                              3358.203
BIC:                                                                115.285
R2:                                                                   0.605
Adj. R2:                                                              0.583

Variable                              Est.         SE  t(Est/SE)    p-value
------------------------------- ---------- ---------- ---------- ----------
X0                                   5.932      0.888      6.682      0.000
X1                                  -4.292      1.303     -3.295      0.001
X2                                  11.508      1.327      8.672      0.000
X3                                  11.263      1.361      8.276      0.000
X4                                  14.579      1.553      9.386      0.000
X5                                   4.428      1.301      3.404      0.001
X6                                  -0.154      0.754     -0.205      0.838
X7                                   0.713      0.959      0.743      0.457
X8                                  -0.882      0.754     -1.171      0.242
X9                                   4.053      1.867      2.171      0.030
X10                                  0.800      1.135      0.704      0.481
X11                                 -0.161      2.564     -0.063      0.950
X12                                 -0.365      1.858     -0.197      0.844
X13                                  1.553      2.793      0.556      0.578
X14                                 -0.281      1.187     -0.236      0.813
X15                                 -0.172      0.733     -0.235      0.814
X16                                 -0.079      0.392     -0.202      0.840
X17                                  0.015      1.328      0.011      0.991
X18                                  0.979      0.680      1.440      0.150
X19                                 -0.216      0.562     -0.384      0.701
X20                                 -1.204      2.698     -0.446      0.655
X21                                 -4.342      1.389     -3.125      0.002
X22                                  0.887      1.320      0.672      0.501
X23                                 -2.340      0.701     -3.338      0.001
X24                                  1.831      0.815      2.247      0.025
X25                                 -0.883      1.424     -0.620      0.535
X26                                 -0.077      0.948     -0.082      0.935
X27                                 -3.394      0.796     -4.263      0.000
X28                                 -0.574      1.471     -0.390      0.696
X29                                 -2.367      1.439     -1.645      0.100
X30                                  0.011      1.087      0.010      0.992
X31                                 -1.195      1.107     -1.079      0.281
X32                                 -2.961      0.950     -3.116      0.002
X33                                  0.840      1.406      0.597      0.550
X34                                  1.571      1.008      1.558      0.119
X35                                 -2.148      1.648     -1.303      0.192
X36                                 -0.036      0.919     -0.039      0.969

Multi-Scale Geographically Weighted Regression (MGWR) Results
---------------------------------------------------------------------------
Spatial kernel:                                           Adaptive gaussian
Criterion for optimal bandwidth:                                        AIC
Score of Change (SOC) type:                                     Smoothing f
Termination criterion for MGWR:                                       1e-05

MGWR bandwidths
---------------------------------------------------------------------------
Variable             Bandwidth      ENP_j   Adj t-val(95%)   Adj alpha(95%)
X0                     698.000      1.006            1.966            0.050
X1                     698.000      1.013            1.969            0.049
X2                      43.000      6.234            2.659            0.008
X3                      43.000      6.649            2.681            0.008
X4                      43.000      5.651            2.625            0.009
X5                      43.000      6.482            2.672            0.008
X6                     696.000      1.046            1.983            0.048
X7                     698.000      1.108            2.007            0.045
X8                     698.000      1.099            2.004            0.046
X9                     566.000      1.027            1.975            0.049
X10                    698.000      1.090            2.000            0.046
X11                    145.000      1.284            2.069            0.039
X12                    698.000      1.038            1.980            0.048
X13                    293.000      1.000            1.963            0.050
X14                    698.000      1.053            1.985            0.047
X15                    698.000      1.114            2.009            0.045
X16                    378.000      1.251            2.058            0.040
X17                    698.000      1.053            1.985            0.047
X18                    698.000      1.095            2.002            0.046
X19                    220.000      2.061            2.258            0.024
X20                    698.000      1.002            1.964            0.050
X21                    698.000      1.018            1.971            0.049
X22                    206.000      1.327            2.082            0.038
X23                    698.000      1.025            1.974            0.049
X24                    642.000      1.068            1.991            0.047
X25                    698.000      1.021            1.972            0.049
X26                    698.000      1.036            1.979            0.048
X27                    698.000      1.020            1.972            0.049
X28                    698.000      1.052            1.985            0.048
X29                    698.000      1.032            1.977            0.048
X30                    698.000      1.016            1.970            0.049
X31                    698.000      1.029            1.975            0.049
X32                     44.000      6.229            2.659            0.008
X33                    698.000      1.008            1.967            0.050
X34                     56.000      3.858            2.491            0.013
X35                    698.000      1.020            1.972            0.049
X36                    698.000      1.030            1.976            0.049

Diagnostic information
---------------------------------------------------------------------------
Residual sum of squares:                                           2888.011
Effective number of parameters (trace(S)):                           69.143
Degree of freedom (n - trace(S)):                                   629.857
Sigma estimate:                                                       2.141
Log-likelihood:                                                   -1487.664
AIC:                                                               3115.615
AICc:                                                              3131.511
BIC:                                                               3434.741
R2                                                                    0.743
Adjusted R2                                                           0.715

Summary Statistics For MGWR Parameter Estimates
---------------------------------------------------------------------------
Variable                   Mean        STD        Min     Median        Max
-------------------- ---------- ---------- ---------- ---------- ----------
X0                        3.662      0.002      3.659      3.662      3.664
X1                       -3.816      0.004     -3.826     -3.813     -3.811
X2                       11.179      1.525      8.056     10.803     15.541
X3                       11.786      5.432      1.618     11.022     28.134
X4                       25.157      8.126     10.613     26.916     38.232
X5                        3.696      2.452     -1.677      3.579     11.155
X6                       -0.763      0.022     -0.790     -0.770     -0.711
X7                        0.328      0.021      0.304      0.322      0.367
X8                       -0.224      0.009     -0.251     -0.221     -0.215
X9                        2.706      0.097      2.494      2.696      2.903
X10                       0.128      0.015      0.104      0.122      0.161
X11                       1.219      2.285     -0.515      0.072      8.494
X12                      -0.218      0.042     -0.309     -0.208     -0.157
X13                       2.108      0.000      2.108      2.108      2.108
X14                       0.055      0.017      0.025      0.057      0.082
X15                      -0.318      0.026     -0.357     -0.320     -0.282
X16                      -0.044      0.050     -0.128     -0.042      0.046
X17                      -0.094      0.015     -0.124     -0.088     -0.073
X18                       0.817      0.018      0.793      0.810      0.864
X19                      -0.383      0.294     -0.918     -0.370      0.064
X20                      -0.418      0.010     -0.435     -0.419     -0.402
X21                      -3.584      0.022     -3.618     -3.585     -3.554
X22                       0.696      0.368      0.017      0.698      1.257
X23                      -1.133      0.018     -1.160     -1.132     -1.107
X24                       1.019      0.036      0.945      1.021      1.090
X25                      -0.208      0.006     -0.219     -0.208     -0.192
X26                      -0.001      0.024     -0.052      0.005      0.034
X27                      -0.331      0.008     -0.341     -0.334     -0.313
X28                      -0.378      0.024     -0.434     -0.378     -0.320
X29                      -2.512      0.018     -2.537     -2.515     -2.454
X30                      -0.924      0.012     -0.952     -0.924     -0.900
X31                      -0.669      0.006     -0.681     -0.669     -0.652
X32                      -1.678      1.259     -3.525     -2.056      1.920
X33                       2.119      0.003      2.114      2.119      2.124
X34                       4.077      2.090      0.692      4.326      8.117
X35                      -2.547      0.004     -2.555     -2.547     -2.537
X36                      -1.195      0.026     -1.233     -1.200     -1.151
===========================================================================


# As reference, here is the (average) R2, AIC, and AICc
print('Mean R2 =', mgwr_results.R2)
print('AIC =', mgwr_results.aic)
print('AICc =', mgwr_results.aicc)

Mean R2 = 0.7433929020383614
AIC = 3115.614543441226
AICc = 3131.5105431729926


mgwr_bw_ci = mgwr_results.get_bws_intervals(mgwr_selector)
print(mgwr_bw_ci)

[(448.0, 698.0), (448.0, 698.0), (43.0, 47.0), (43.0, 51.0), (43.0, 47.0), (43.0, 47.0), (448.0, 696.0), (448.0, 698.0), (448.0, 698.0), (448.0, 566.0), (448.0, 698.0), (138.0, 293.0), (448.0, 698.0), (293.0, 293.0), (448.0, 698.0), (448.0, 698.0), (375.0, 448.0), (448.0, 698.0), (448.0, 698.0), (220.0, 293.0), (448.0, 698.0), (448.0, 698.0), (197.0, 293.0), (448.0, 698.0), (448.0, 642.0), (448.0, 698.0), (448.0, 698.0), (448.0, 698.0), (448.0, 698.0), (448.0, 698.0), (448.0, 698.0), (448.0, 698.0), (44.0, 101.0), (448.0, 698.0), (56.0, 101.0), (448.0, 698.0), (448.0, 698.0)]


#Copy to preserve original gdf
gdf_mr = gdf.copy()

# Local R2 are NOT yet implemented in MGWR
#mgwr_results.localR2[0:5]


gdf_mr['res'] = mgwr_results.resid_response
fig, ax = plt.subplots(figsize=(6, 6))
gdf_mr.plot(column='res', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)
ax.set_title('Local Residuals', fontsize=12)
ax.axis("off")
#plt.savefig('myMap.png',dpi=150, bbox_inches='tight')
plt.show()


#plot distribution
import seaborn as sns
sns.histplot(data=gdf_mr, x="res")

<AxesSubplot: xlabel='res', ylabel='Count'>


#Store coef column names

coef_names = []
for num,column in enumerate (X_df.columns):


    gdf_mr['gwr_{}'.format(column)] = mgwr_results.params[:,num+1]
    
    coef_names.append('gwr_{}'.format(column))
gdf_mr.head()


#creating copy()
gdf_abs_m =gdf_mr.copy()
gdf_abs_m

#acum per coeficient
mean_coef= []
#getting abs of each var
for num,column in enumerate ('gwr_'+X_df.columns):
    gdf_abs_m[column] = gdf_abs_m[column].apply(lambda x : abs(x))
    mean_coef.append(gdf_abs_m[column].mean())
gdf_abs_m

df = pd.DataFrame(mean_coef).T
df.columns= X_df.columns
df = df.T.reset_index().rename(columns={'index': 'variable', 0: "coef_abs_value"})
df_coefs_m = df.sort_values(by= "coef_abs_value" )
df_coefs_m.tail(20)


plt.figure(figsize=(3, 20))
plt.barh(df_coefs_m["variable"][20:], df_coefs_m["coef_abs_value"][20:], height=0.2, color='blue', alpha=0.8, align='center', linewidth=0)

<BarContainer object of 16 artists>


#Store coef column names
coef_names = []
for num,column in enumerate (X_df.columns):


    gdf_mr['gwr_{}'.format(column)] = mgwr_results.params[:,num+1]
    coef_names.append('gwr_{}'.format(column))
gdf_mr.head()


# Filter t-values: standard alpha = 0.05
mgwr_filtered_t = mgwr_results.filter_tvals(alpha = 0.05)
# Filter t-values: corrected alpha due to multiple testing
mgwr_filtered_tc = mgwr_results.filter_tvals()


for num,col in enumerate (coef_names):

    try:
        fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18,6))

        gdf_mr.plot(column= col, cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=axes[0])

        gdf_mr.plot(column= col, cmap = 'coolwarm', linewidth=0.05, scheme = 'FisherJenks', k=5, legend=False, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=axes[1])
        gdf_mr[gwr_filtered_t[:,num+1] == 0].plot(color='white', linewidth=0.05, edgecolor='black', ax=axes[1])


        gdf_mr.plot(column= col, cmap = 'coolwarm', linewidth=0.05, scheme = 'FisherJenks', k=5, legend=False, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=axes[2])
        gdf_mr[gwr_filtered_tc[:,num+1] == 0].plot(color='white', linewidth=0.05, edgecolor='black', ax=axes[2])

        plt.tight_layout()

        axes[0].axis("off")
        axes[1].axis("off")
        axes[2].axis("off")

        axes[0].set_title('(a) '+ col +' (BW: ' + str(gwr_bw) +'), all coeffs', fontsize=12)
        axes[1].set_title('(b) '+ col+' (BW: ' + str(gwr_bw) +'), significant coeffs',     fontsize=12)
        axes[2].set_title('(c) '+col +' (BW: ' + str(gwr_bw) +'), significant coeffs and corr. p-values',     fontsize=12)
        plt.show()
    except:
        pass


%%time
# Monte Carlo test of spatial variability: 10 iterations 
mgwr_p_values_stationarity = mgwr_results.spatial_variability(mgwr_selector, 10)

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df = pd.DataFrame(mgwr_p_values_stationarity[1:]).T
df.columns =  coef_names
df


cols_variation = df.columns[(df < 0.05).any()]
print("cols with spatial variability: ",cols_variation )

cols with spatial variability:  Index(['gwr_half_bathrooms', 'gwr_lot_size', 'gwr_is_house', 'gwr_museum',
       'gwr_industry'],
      dtype='object')


mgwrCN, mgwrVDP = mgwr_results.local_collinearity()


gdf_mr['mgwr_CN'] = mgwrCN


fig, ax = plt.subplots(figsize=(6, 6))
gdf_mr.plot(column='mgwr_CN', cmap = 'coolwarm', linewidth=0.01, scheme = 'FisherJenks', k=5, legend=True, legend_kwds={'bbox_to_anchor':(1.10, 0.96)},  ax=ax)
ax.set_title('Local multicollinearity (CN > 30)?', fontsize=12)
ax.axis("off")
#plt.savefig('myMap.png',dpi=150, bbox_inches='tight')
plt.show()

	bedrooms	bathrooms	half_bathrooms	lot_size	construction_size	age	is_house	real_age	latitude	longitude	balcony	water strorage	air_conditioning	pool	furniture	accasible_for_handicap	first_floor	security	parking_space	kitchen	garden	roof_Garden	service_room	gated_community	price_per_area	price_per_area_small	high_impact/area	low_impact/area	parks	schools	university	hospital	sport_facility	supermarket	mall	stadium	historic	museum	airport	industry	subway	bus	id_right	price	price_small	geometry
0	1.607506	3.795655	-0.782058	-0.090367	0.101543	0.274983	1.189032	2.965947	1.189645	-0.527365	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.686690	-0.686690	-0.362235	-0.490653	-1.204725	-0.026416	-0.190933	0.217603	-0.937668	-0.393369	-0.428634	0.059922	-0.030999	0.142419	-0.086261	-0.280822	-0.360175	-0.457558	8	3.400000e+06	3.400000	MULTIPOLYGON (((2793299.849 832180.730, 279330...
1	-0.789660	-1.259302	-0.782058	-0.164097	-0.170815	-0.151405	-0.912587	0.167806	0.928072	-0.362927	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	0.468556	0.468556	1.776640	1.564577	-1.733903	0.478983	-0.620477	0.066453	-1.146581	-0.490887	-0.834471	0.076028	-0.273689	-0.313227	-0.352554	-0.992470	-0.777304	-0.857703	16	2.539500e+06	2.539500	MULTIPOLYGON (((2794357.004 830240.705, 279438...
2	-1.588716	-1.259302	-0.782058	-0.141148	-0.147499	-0.342970	-0.912587	-1.089330	0.628836	-0.654206	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-1.299618	-0.256962	-0.377890	-0.483958	-0.041937	2.725285	2.725285	0.106282	1.299354	0.423617	2.150015	-0.493853	-1.058884	-0.739339	-0.711208	-0.331797	0.405543	-0.918067	-0.833031	-0.101611	0.366546	-0.586630	0.407494	33	6.950000e+06	6.950000	MULTIPOLYGON (((2792835.890 828193.326, 279284...
3	5.602784	1.773672	-0.782058	-0.071812	0.169013	-0.342970	1.189032	-1.089330	0.492163	0.801185	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	0.409655	0.409655	-0.181528	0.331494	-0.788814	-0.642095	1.123594	0.069091	-0.167126	-0.450806	-0.580005	-0.407965	-0.400672	-0.088077	-1.532088	-0.895682	-0.684277	-0.365462	34	7.000000e+06	7.000000	MULTIPOLYGON (((2801132.447 826978.129, 280115...
4	0.009395	-0.248311	-0.782058	-0.089390	-0.104834	-0.046353	1.189032	0.857203	0.946030	-0.581633	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.353675	-0.353675	1.787594	1.277233	-0.560837	0.465191	-0.118446	-0.554636	-1.216916	-0.175668	-0.721703	0.592396	-0.471636	-0.921843	-0.131758	-0.528284	-0.702726	-0.678414	35	4.250000e+06	4.250000	MULTIPOLYGON (((2793254.156 830422.310, 279325...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
694	0.009395	0.560482	1.531052	0.321350	0.125157	-0.177359	1.189032	-0.002516	-1.787337	0.108172	1.553010	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-0.723069	-0.256962	1.992182	0.465006	-0.041937	-1.409726	-1.409726	-1.083751	-1.166632	3.268392	0.924926	0.253718	-0.359049	0.924474	0.268487	1.497203	-1.110881	0.981668	1.568479	1.463781	2.170619	1.131246	-0.605667	1559	5.742400e+06	5.742400	MULTIPOLYGON (((2796977.276 808648.302, 279697...
695	0.009395	0.762681	1.145533	-0.047887	0.000835	-0.293534	1.189032	-0.764908	-0.834495	0.677211	-0.280675	3.157024	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	0.141754	-0.256962	-0.377890	-0.483958	-0.041937	0.499523	0.499523	-0.539563	-0.659075	-0.701303	-0.247120	-1.099828	-0.446943	0.503482	-0.350285	-0.811326	-0.702828	-0.623495	-0.368603	0.021319	-0.692475	-0.637533	-0.342341	1561	8.500000e+06	8.500000	MULTIPOLYGON (((2800395.769 816279.216, 280039...
696	0.808451	0.762681	1.145533	-0.019078	0.075746	-0.021635	1.189032	1.019414	-0.875106	0.765444	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.079408	-0.079408	-0.610944	-0.131753	-1.292936	0.684953	-0.942193	-0.829277	0.607015	-0.609447	-0.942246	-0.580451	-1.096245	0.074720	0.046001	-0.401229	-0.435141	-0.669519	1562	7.700000e+06	7.700000	MULTIPOLYGON (((2800691.441 815947.949, 280075...
697	0.009395	1.268176	1.145533	-0.058141	-0.001149	0.058699	1.189032	1.546600	-1.318348	-0.281820	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	0.141754	-0.256962	-0.377890	1.888453	-0.041937	0.257155	0.257155	-0.711344	-0.666830	-1.261227	-0.503962	-0.815370	-1.011149	-0.160907	0.056774	-0.746703	0.014316	-0.162428	-0.217427	1.080662	1.123871	0.560929	1.742792	1563	7.200000e+06	7.200000	MULTIPOLYGON (((2794204.043 812033.062, 279422...
698	1.074802	0.088686	-0.139527	0.089971	-0.008591	-0.219379	0.488492	-0.278274	-1.936461	-0.853059	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	1.714936	-0.30405	0.622211	-0.256962	-0.377890	-0.483958	-0.041937	-0.842773	-0.842773	-1.214451	-1.331140	2.662116	0.863570	1.231311	2.085989	1.121596	1.685666	2.827583	2.072800	1.643410	3.282936	1.973052	4.681654	2.358849	2.188619	1564	5.943333e+06	5.943333	MULTIPOLYGON (((2782382.857 804523.930, 278242...

	bedrooms	bathrooms	half_bathrooms	lot_size	is_house	real_age	balcony	water strorage	air_conditioning	pool	furniture	accasible_for_handicap	first_floor	security	parking_space	kitchen	garden	roof_Garden	service_room	gated_community	high_impact/area	low_impact/area	parks	schools	university	hospital	sport_facility	supermarket	mall	stadium	historic	museum	airport	industry	subway	bus
0	1.607506	3.795655	-0.782058	-0.090367	1.189032	2.965947	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.362235	-0.490653	-1.204725	-0.026416	-0.190933	0.217603	-0.937668	-0.393369	-0.428634	0.059922	-0.030999	0.142419	-0.086261	-0.280822	-0.360175	-0.457558
1	-0.789660	-1.259302	-0.782058	-0.164097	-0.912587	0.167806	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	1.776640	1.564577	-1.733903	0.478983	-0.620477	0.066453	-1.146581	-0.490887	-0.834471	0.076028	-0.273689	-0.313227	-0.352554	-0.992470	-0.777304	-0.857703
2	-1.588716	-1.259302	-0.782058	-0.141148	-0.912587	-1.089330	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-1.299618	-0.256962	-0.377890	-0.483958	-0.041937	0.106282	1.299354	0.423617	2.150015	-0.493853	-1.058884	-0.739339	-0.711208	-0.331797	0.405543	-0.918067	-0.833031	-0.101611	0.366546	-0.586630	0.407494
3	5.602784	1.773672	-0.782058	-0.071812	1.189032	-1.089330	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.181528	0.331494	-0.788814	-0.642095	1.123594	0.069091	-0.167126	-0.450806	-0.580005	-0.407965	-0.400672	-0.088077	-1.532088	-0.895682	-0.684277	-0.365462
4	0.009395	-0.248311	-0.782058	-0.089390	1.189032	0.857203	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	1.787594	1.277233	-0.560837	0.465191	-0.118446	-0.554636	-1.216916	-0.175668	-0.721703	0.592396	-0.471636	-0.921843	-0.131758	-0.528284	-0.702726	-0.678414
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
694	0.009395	0.560482	1.531052	0.321350	1.189032	-0.002516	1.553010	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-0.723069	-0.256962	1.992182	0.465006	-0.041937	-1.083751	-1.166632	3.268392	0.924926	0.253718	-0.359049	0.924474	0.268487	1.497203	-1.110881	0.981668	1.568479	1.463781	2.170619	1.131246	-0.605667
695	0.009395	0.762681	1.145533	-0.047887	1.189032	-0.764908	-0.280675	3.157024	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	0.141754	-0.256962	-0.377890	-0.483958	-0.041937	-0.539563	-0.659075	-0.701303	-0.247120	-1.099828	-0.446943	0.503482	-0.350285	-0.811326	-0.702828	-0.623495	-0.368603	0.021319	-0.692475	-0.637533	-0.342341
696	0.808451	0.762681	1.145533	-0.019078	1.189032	1.019414	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.610944	-0.131753	-1.292936	0.684953	-0.942193	-0.829277	0.607015	-0.609447	-0.942246	-0.580451	-1.096245	0.074720	0.046001	-0.401229	-0.435141	-0.669519
697	0.009395	1.268176	1.145533	-0.058141	1.189032	1.546600	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	0.141754	-0.256962	-0.377890	1.888453	-0.041937	-0.711344	-0.666830	-1.261227	-0.503962	-0.815370	-1.011149	-0.160907	0.056774	-0.746703	0.014316	-0.162428	-0.217427	1.080662	1.123871	0.560929	1.742792
698	1.074802	0.088686	-0.139527	0.089971	0.488492	-0.278274	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	1.714936	-0.30405	0.622211	-0.256962	-0.377890	-0.483958	-0.041937	-1.214451	-1.331140	2.662116	0.863570	1.231311	2.085989	1.121596	1.685666	2.827583	2.072800	1.643410	3.282936	1.973052	4.681654	2.358849	2.188619

	bedrooms	bathrooms	half_bathrooms	lot_size	construction_size	age	is_house	real_age	balcony	water strorage	air_conditioning	pool	furniture	accasible_for_handicap	first_floor	security	parking_space	kitchen	garden	roof_Garden	service_room	gated_community	high_impact/area	low_impact/area	parks	schools	university	hospital	sport_facility	supermarket	mall	stadium	historic	museum	airport	industry	subway	bus	id_right	geometry	centroids
0	1.607506	3.795655	-0.782058	-0.090367	0.101543	0.274983	1.189032	2.965947	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.362235	-0.490653	-1.204725	-0.026416	-0.190933	0.217603	-0.937668	-0.393369	-0.428634	0.059922	-0.030999	0.142419	-0.086261	-0.280822	-0.360175	-0.457558	8	MULTIPOLYGON (((2793299.849 832180.730, 279330...	POINT (2793471.544 832294.395)
1	-0.789660	-1.259302	-0.782058	-0.164097	-0.170815	-0.151405	-0.912587	0.167806	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	1.776640	1.564577	-1.733903	0.478983	-0.620477	0.066453	-1.146581	-0.490887	-0.834471	0.076028	-0.273689	-0.313227	-0.352554	-0.992470	-0.777304	-0.857703	16	MULTIPOLYGON (((2794357.004 830240.705, 279438...	POINT (2794483.270 830330.304)
2	-1.588716	-1.259302	-0.782058	-0.141148	-0.147499	-0.342970	-0.912587	-1.089330	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-1.299618	-0.256962	-0.377890	-0.483958	-0.041937	0.106282	1.299354	0.423617	2.150015	-0.493853	-1.058884	-0.739339	-0.711208	-0.331797	0.405543	-0.918067	-0.833031	-0.101611	0.366546	-0.586630	0.407494	33	MULTIPOLYGON (((2792835.890 828193.326, 279284...	POINT (2792847.230 827937.598)
3	5.602784	1.773672	-0.782058	-0.071812	0.169013	-0.342970	1.189032	-1.089330	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.181528	0.331494	-0.788814	-0.642095	1.123594	0.069091	-0.167126	-0.450806	-0.580005	-0.407965	-0.400672	-0.088077	-1.532088	-0.895682	-0.684277	-0.365462	34	MULTIPOLYGON (((2801132.447 826978.129, 280115...	POINT (2801276.300 826921.114)
4	0.009395	-0.248311	-0.782058	-0.089390	-0.104834	-0.046353	1.189032	0.857203	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	1.787594	1.277233	-0.560837	0.465191	-0.118446	-0.554636	-1.216916	-0.175668	-0.721703	0.592396	-0.471636	-0.921843	-0.131758	-0.528284	-0.702726	-0.678414	35	MULTIPOLYGON (((2793254.156 830422.310, 279325...	POINT (2793441.628 830458.857)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
694	0.009395	0.560482	1.531052	0.321350	0.125157	-0.177359	1.189032	-0.002516	1.553010	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-0.723069	-0.256962	1.992182	0.465006	-0.041937	-1.083751	-1.166632	3.268392	0.924926	0.253718	-0.359049	0.924474	0.268487	1.497203	-1.110881	0.981668	1.568479	1.463781	2.170619	1.131246	-0.605667	1559	MULTIPOLYGON (((2796977.276 808648.302, 279697...	POINT (2798141.268 808045.708)
695	0.009395	0.762681	1.145533	-0.047887	0.000835	-0.293534	1.189032	-0.764908	-0.280675	3.157024	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	0.141754	-0.256962	-0.377890	-0.483958	-0.041937	-0.539563	-0.659075	-0.701303	-0.247120	-1.099828	-0.446943	0.503482	-0.350285	-0.811326	-0.702828	-0.623495	-0.368603	0.021319	-0.692475	-0.637533	-0.342341	1561	MULTIPOLYGON (((2800395.769 816279.216, 280039...	POINT (2800676.582 816323.980)
696	0.808451	0.762681	1.145533	-0.019078	0.075746	-0.021635	1.189032	1.019414	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.377890	-0.483958	-0.041937	-0.610944	-0.131753	-1.292936	0.684953	-0.942193	-0.829277	0.607015	-0.609447	-0.942246	-0.580451	-1.096245	0.074720	0.046001	-0.401229	-0.435141	-0.669519	1562	MULTIPOLYGON (((2800691.441 815947.949, 280075...	POINT (2801098.564 815960.158)
697	0.009395	1.268176	1.145533	-0.058141	-0.001149	0.058699	1.189032	1.546600	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	0.141754	-0.256962	-0.377890	1.888453	-0.041937	-0.711344	-0.666830	-1.261227	-0.503962	-0.815370	-1.011149	-0.160907	0.056774	-0.746703	0.014316	-0.162428	-0.217427	1.080662	1.123871	0.560929	1.742792	1563	MULTIPOLYGON (((2794204.043 812033.062, 279422...	POINT (2795124.280 812357.193)
698	1.074802	0.088686	-0.139527	0.089971	-0.008591	-0.219379	0.488492	-0.278274	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	1.714936	-0.30405	0.622211	-0.256962	-0.377890	-0.483958	-0.041937	-1.214451	-1.331140	2.662116	0.863570	1.231311	2.085989	1.121596	1.685666	2.827583	2.072800	1.643410	3.282936	1.973052	4.681654	2.358849	2.188619	1564	MULTIPOLYGON (((2782382.857 804523.930, 278242...	POINT (2792055.722 800552.135)

	bedrooms	bathrooms	half_bathrooms	lot_size	construction_size	age	is_house	real_age	latitude	longitude	balcony	water strorage	air_conditioning	pool	furniture	accasible_for_handicap	first_floor	security	parking_space	kitchen	garden	roof_Garden	service_room	gated_community	price_per_area	price_per_area_small	high_impact/area	low_impact/area	parks	schools	university	hospital	sport_facility	supermarket	mall	stadium	historic	museum	airport	industry	subway	bus	id_right	price	price_small	geometry	centroids	gwr_R2	res	gwr_bedrooms	gwr_bathrooms	gwr_half_bathrooms	gwr_lot_size	gwr_is_house	gwr_real_age	gwr_balcony	gwr_water strorage	gwr_air_conditioning	gwr_pool	gwr_furniture	gwr_accasible_for_handicap	gwr_first_floor	gwr_security	gwr_parking_space	gwr_kitchen	gwr_garden	gwr_roof_Garden	gwr_service_room	gwr_gated_community	gwr_high_impact/area	gwr_low_impact/area	gwr_parks	gwr_schools	gwr_university	gwr_hospital	gwr_sport_facility	gwr_supermarket	gwr_mall	gwr_stadium	gwr_historic	gwr_museum	gwr_airport	gwr_industry	gwr_subway	gwr_bus
0	1.607506	3.795655	-0.782058	-0.090367	0.101543	0.274983	1.189032	2.965947	1.189645	-0.527365	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	-0.686690	-0.686690	-0.362235	-0.490653	-1.204725	-0.026416	-0.190933	0.217603	-0.937668	-0.393369	-0.428634	0.059922	-0.030999	0.142419	-0.086261	-0.280822	-0.360175	-0.457558	8	3400000.0	3.4000	MULTIPOLYGON (((2793299.849 832180.730, 279330...	POINT (2793471.544 832294.395)	0.687348	-4.647510	-3.466279	7.591192	10.926504	32.638213	3.970206	-2.313301	1.985543	0.314177	4.618202	0.085514	-0.420987	1.915057	0.185340	-0.192532	0.625514	0.912312	0.156981	0.375939	0.645254	0.534043	-4.512988	3.245974	-0.537155	1.070957	2.814521	1.217922	-0.489518	-2.423590	-4.948959	0.496560	-3.899463	-4.150380	6.365939	7.048493	-5.351782	-0.055947
1	-0.789660	-1.259302	-0.782058	-0.164097	-0.170815	-0.151405	-0.912587	0.167806	0.928072	-0.362927	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	0.468556	0.468556	1.776640	1.564577	-1.733903	0.478983	-0.620477	0.066453	-1.146581	-0.490887	-0.834471	0.076028	-0.273689	-0.313227	-0.352554	-0.992470	-0.777304	-0.857703	16	2539500.0	2.5395	MULTIPOLYGON (((2794357.004 830240.705, 279438...	POINT (2794483.270 830330.304)	0.690056	-0.475401	-3.389462	7.662344	11.874993	32.484372	4.531533	-2.420625	1.969117	0.356181	4.723062	-0.073963	-0.220741	1.487627	0.321187	-0.340734	0.655488	0.965648	0.404535	0.248710	0.666898	1.081344	-4.220376	2.821909	-0.738791	1.191683	2.434812	0.966404	-0.674097	-2.864939	-4.336693	0.455353	-3.892113	-3.958200	6.167527	7.019150	-4.956193	-0.609605
2	-1.588716	-1.259302	-0.782058	-0.141148	-0.147499	-0.342970	-0.912587	-1.089330	0.628836	-0.654206	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-1.299618	-0.256962	-0.37789	-0.483958	-0.041937	2.725285	2.725285	0.106282	1.299354	0.423617	2.150015	-0.493853	-1.058884	-0.739339	-0.711208	-0.331797	0.405543	-0.918067	-0.833031	-0.101611	0.366546	-0.586630	0.407494	33	6950000.0	6.9500	MULTIPOLYGON (((2792835.890 828193.326, 279284...	POINT (2792847.230 827937.598)	0.688815	1.068863	-4.692179	8.610435	14.160388	26.848484	6.035374	-2.469671	2.241921	0.156148	4.283251	-0.225847	0.282789	1.515921	1.766349	0.274273	0.479384	1.116042	0.081439	0.424891	0.918453	-0.563360	-3.995299	1.627192	-1.174085	2.116030	0.204304	0.616913	-0.535716	-4.987554	-5.814604	1.534451	-2.947654	-3.122525	5.011318	6.516866	-6.465580	-0.442079
3	5.602784	1.773672	-0.782058	-0.071812	0.169013	-0.342970	1.189032	-1.089330	0.492163	0.801185	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	0.409655	0.409655	-0.181528	0.331494	-0.788814	-0.642095	1.123594	0.069091	-0.167126	-0.450806	-0.580005	-0.407965	-0.400672	-0.088077	-1.532088	-0.895682	-0.684277	-0.365462	34	7000000.0	7.0000	MULTIPOLYGON (((2801132.447 826978.129, 280115...	POINT (2801276.300 826921.114)	0.775243	1.048679	-2.090263	7.867392	8.627419	39.689562	3.981691	-1.699026	0.883609	-0.109151	5.478190	0.249622	-0.570211	-0.464335	2.007378	-0.684650	0.879572	0.176102	0.363881	0.601585	0.508567	1.065705	-2.592936	0.484785	-1.767813	1.161393	-1.491649	-0.065161	-2.139503	-2.481587	-0.308648	-1.135564	-1.278347	-3.618492	1.214845	4.785853	-2.845756	0.186557
4	0.009395	-0.248311	-0.782058	-0.089390	-0.104834	-0.046353	1.189032	0.857203	0.946030	-0.581633	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	-0.353675	-0.353675	1.787594	1.277233	-0.560837	0.465191	-0.118446	-0.554636	-1.216916	-0.175668	-0.721703	0.592396	-0.471636	-0.921843	-0.131758	-0.528284	-0.702726	-0.678414	35	4250000.0	4.2500	MULTIPOLYGON (((2793254.156 830422.310, 279325...	POINT (2793441.628 830458.857)	0.687507	-2.237245	-3.854145	7.870895	12.548724	30.440394	4.835266	-2.480039	2.165290	0.319951	4.503532	-0.100889	-0.159045	1.735331	0.651452	-0.085893	0.600498	1.017488	0.213183	0.201167	0.718178	-0.008039	-4.324442	2.788384	-0.782325	1.447043	2.097704	0.925380	-0.415162	-3.067125	-5.022254	0.764160	-3.713259	-3.818653	6.169184	6.986260	-5.601593	-0.370930

	bedrooms	bathrooms	half_bathrooms	lot_size	construction_size	age	is_house	real_age	latitude	longitude	balcony	water strorage	air_conditioning	pool	furniture	accasible_for_handicap	first_floor	security	parking_space	kitchen	garden	roof_Garden	service_room	gated_community	price_per_area	price_per_area_small	high_impact/area	low_impact/area	parks	schools	university	hospital	sport_facility	supermarket	mall	stadium	historic	museum	airport	industry	subway	bus	id_right	price	price_small	geometry	centroids	res	gwr_bedrooms	gwr_bathrooms	gwr_half_bathrooms	gwr_lot_size	gwr_is_house	gwr_real_age	gwr_balcony	gwr_water strorage	gwr_air_conditioning	gwr_pool	gwr_furniture	gwr_accasible_for_handicap	gwr_first_floor	gwr_security	gwr_parking_space	gwr_kitchen	gwr_garden	gwr_roof_Garden	gwr_service_room	gwr_gated_community	gwr_high_impact/area	gwr_low_impact/area	gwr_parks	gwr_schools	gwr_university	gwr_hospital	gwr_sport_facility	gwr_supermarket	gwr_mall	gwr_stadium	gwr_historic	gwr_museum	gwr_airport	gwr_industry	gwr_subway	gwr_bus	mgwr_CN
0	1.607506	3.795655	-0.782058	-0.090367	0.101543	0.274983	1.189032	2.965947	1.189645	-0.527365	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	-0.686690	-0.686690	-0.362235	-0.490653	-1.204725	-0.026416	-0.190933	0.217603	-0.937668	-0.393369	-0.428634	0.059922	-0.030999	0.142419	-0.086261	-0.280822	-0.360175	-0.457558	8	3400000.0	3.4000	MULTIPOLYGON (((2793299.849 832180.730, 279330...	POINT (2793471.544 832294.395)	-4.093855	-3.820705	8.209239	21.765846	33.740222	-1.532007	-0.789502	0.355744	-0.216109	2.636711	0.118416	-0.257722	-0.160736	2.108294	0.041690	-0.286736	0.016856	-0.108750	0.795252	-0.078957	-0.412212	-3.558356	1.180592	-1.110475	1.047795	-0.207029	0.030967	-0.337496	-0.376696	-2.526685	-0.912032	-0.667024	-2.730806	2.123385	7.502621	-2.541529	-1.166609	46.206861
1	-0.789660	-1.259302	-0.782058	-0.164097	-0.170815	-0.151405	-0.912587	0.167806	0.928072	-0.362927	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	0.468556	0.468556	1.776640	1.564577	-1.733903	0.478983	-0.620477	0.066453	-1.146581	-0.490887	-0.834471	0.076028	-0.273689	-0.313227	-0.352554	-0.992470	-0.777304	-0.857703	16	2539500.0	2.5395	MULTIPOLYGON (((2794357.004 830240.705, 279438...	POINT (2794483.270 830330.304)	0.174632	-3.819556	10.159057	26.360601	31.467555	-0.067571	-0.788091	0.351431	-0.216012	2.676330	0.116205	-0.251113	-0.165538	2.108294	0.042452	-0.288776	0.024435	-0.106341	0.795833	-0.054597	-0.411771	-3.559843	1.170225	-1.112670	1.055573	-0.208364	0.028083	-0.338003	-0.384538	-2.527654	-0.913828	-0.668924	-2.778364	2.122766	7.728018	-2.543066	-1.169085	44.469092
2	-1.588716	-1.259302	-0.782058	-0.141148	-0.147499	-0.342970	-0.912587	-1.089330	0.628836	-0.654206	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	-1.299618	-0.256962	-0.37789	-0.483958	-0.041937	2.725285	2.725285	0.106282	1.299354	0.423617	2.150015	-0.493853	-1.058884	-0.739339	-0.711208	-0.331797	0.405543	-0.918067	-0.833031	-0.101611	0.366546	-0.586630	0.407494	33	6950000.0	6.9500	MULTIPOLYGON (((2792835.890 828193.326, 279284...	POINT (2792847.230 827937.598)	1.837461	-3.817511	10.625324	19.025260	22.997655	3.811676	-0.785415	0.344069	-0.215238	2.673781	0.119439	0.040957	-0.168290	2.108294	0.049244	-0.296095	0.041541	-0.099402	0.795505	-0.131794	-0.415535	-3.565749	1.063377	-1.116621	1.080070	-0.206712	0.026585	-0.336468	-0.380716	-2.531256	-0.911282	-0.667586	-1.142486	2.122353	7.849862	-2.542798	-1.178754	44.997636
3	5.602784	1.773672	-0.782058	-0.071812	0.169013	-0.342970	1.189032	-1.089330	0.492163	0.801185	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	0.409655	0.409655	-0.181528	0.331494	-0.788814	-0.642095	1.123594	0.069091	-0.167126	-0.450806	-0.580005	-0.407965	-0.400672	-0.088077	-1.532088	-0.895682	-0.684277	-0.365462	34	7000000.0	7.0000	MULTIPOLYGON (((2801132.447 826978.129, 280115...	POINT (2801276.300 826921.114)	1.521687	-3.817239	10.456644	7.936986	33.730894	4.525735	-0.774326	0.340428	-0.222042	2.831699	0.105249	-0.474197	-0.206047	2.108294	0.034044	-0.294237	-0.053565	-0.107927	0.809909	-0.165440	-0.403898	-3.563374	0.916943	-1.121447	1.023333	-0.216799	0.006303	-0.340101	-0.414631	-2.510911	-0.932691	-0.678375	-2.094836	2.119325	4.742787	-2.550426	-1.168359	51.024642
4	0.009395	-0.248311	-0.782058	-0.089390	-0.104834	-0.046353	1.189032	0.857203	0.946030	-0.581633	-0.280675	-0.319698	-0.078457	-0.178423	-0.060755	-0.124474	-0.037796	-0.292728	-0.30405	1.583125	-0.256962	-0.37789	-0.483958	-0.041937	-0.353675	-0.353675	1.787594	1.277233	-0.560837	0.465191	-0.118446	-0.554636	-1.216916	-0.175668	-0.721703	0.592396	-0.471636	-0.921843	-0.131758	-0.528284	-0.702726	-0.678414	35	4250000.0	4.2500	MULTIPOLYGON (((2793254.156 830422.310, 279325...	POINT (2793441.628 830458.857)	-0.217829	-3.819480	10.004128	25.832383	28.472873	-0.277485	-0.787946	0.351578	-0.215724	2.656195	0.118069	-0.182458	-0.163354	2.108294	0.044034	-0.289620	0.028829	-0.105516	0.795154	-0.092155	-0.413060	-3.560620	1.151579	-1.112559	1.058624	-0.207164	0.029407	-0.337327	-0.379616	-2.528455	-0.912011	-0.667550	-2.589323	2.123009	7.953560	-2.542138	-1.170539	45.701609

IMPORT DATA¶

Select variables (y, X) and define coordinates¶

Test local Autocorrelation Moran cluster map¶

Estimate GWR model¶

Select GWR bandwith¶

Fit GWR model¶

Plot Residuals¶

Plot coefficients GLOBAL¶

Plot coefficients LOCAL¶

Test spatial stationarity or non stationarity( heteroginity)?¶

Test Local multicollinearity¶

MGWR¶

Show bandwidth intervals¶

Plot Residuals¶

Plot coefficients GLOBAL¶

Plot coefficients Local¶

Test spatial stationarity¶

Test Local multicollinearity¶

Main Reference:¶

	0	1
0	0	{29: 0.125, 303: 0.125, 574: 0.125, 678: 0.125...
1	1	{5: 0.125, 516: 0.125, 306: 0.125, 6: 0.125, 1...
2	2	{416: 0.125, 157: 0.125, 311: 0.125, 299: 0.12...
3	3	{101: 0.125, 247: 0.125, 33: 0.125, 208: 0.125...
4	4	{572: 0.125, 13: 0.125, 49: 0.125, 74: 0.125, ...
...	...	...
694	694	{639: 0.125, 281: 0.125, 608: 0.125, 494: 0.12...
695	695	{374: 0.125, 472: 0.125, 696: 0.125, 436: 0.12...
696	696	{436: 0.125, 695: 0.125, 374: 0.125, 410: 0.12...
697	697	{672: 0.125, 603: 0.125, 643: 0.125, 658: 0.12...
698	698	{433: 0.125, 158: 0.125, 610: 0.125, 639: 0.12...

	variable	coef_abs_value
21	low_impact/area	2.114240
29	stadium	2.237494
32	airport	2.405018
24	university	2.411851
12	first_floor	2.690040
19	gated_community	2.808483
28	mall	2.915253
31	museum	3.191003
27	supermarket	3.547586
20	high_impact/area	3.695992
0	bedrooms	3.847962
26	sport_facility	3.923044
33	industry	4.010242
10	furniture	4.018642
34	subway	4.504926
4	is_house	5.124622
8	air_conditioning	5.526732
1	bathrooms	9.966473
2	half_bathrooms	10.859475
3	lot_size	26.916191

	gwr_bedrooms	gwr_bathrooms	gwr_half_bathrooms	gwr_lot_size	gwr_is_house	gwr_real_age	gwr_balcony	gwr_water strorage	gwr_air_conditioning	gwr_pool	gwr_furniture	gwr_accasible_for_handicap	gwr_first_floor	gwr_security	gwr_parking_space	gwr_kitchen	gwr_garden	gwr_roof_Garden	gwr_service_room	gwr_gated_community	gwr_high_impact/area	gwr_low_impact/area	gwr_parks	gwr_schools	gwr_university	gwr_hospital	gwr_sport_facility	gwr_supermarket	gwr_mall	gwr_stadium	gwr_historic	gwr_museum	gwr_airport	gwr_industry	gwr_subway	gwr_bus
count	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00	699.00
mean	2.65	2.90	1.56	1.98	2.60	1.51	1.18	1.28	1.06	1.33	1.05	1.08	1.08	4.56	1.36	2.01	4.16	1.44	1.48	1.07	3.04	3.28	1.36	1.46	4.31	1.90	2.41	2.00	3.06	3.71	2.68	2.33	9.03	2.24	11.59	1.60
std	0.44	0.27	0.10	0.26	0.26	0.12	0.05	0.09	0.03	0.15	0.04	0.01	0.14	1.64	0.12	0.17	1.56	0.20	0.20	0.09	0.63	0.49	0.17	0.14	1.36	0.22	0.56	0.72	1.65	2.54	0.68	0.43	3.38	0.81	9.22	0.23
min	2.15	2.36	1.40	1.63	2.26	1.30	1.12	1.16	1.01	1.12	1.00	1.05	1.00	2.34	1.24	1.81	2.18	1.16	1.23	1.01	2.28	2.70	1.11	1.26	2.38	1.56	1.45	1.37	1.47	1.54	1.87	1.62	4.23	1.37	2.27	1.28
25%	2.31	2.68	1.47	1.76	2.38	1.41	1.15	1.20	1.04	1.21	1.02	1.07	1.00	3.27	1.26	1.88	2.88	1.27	1.32	1.01	2.45	2.79	1.22	1.35	3.01	1.70	1.93	1.54	1.78	1.95	2.03	1.98	5.76	1.59	3.72	1.47
50%	2.52	2.92	1.56	1.89	2.53	1.52	1.17	1.27	1.06	1.30	1.04	1.08	1.02	4.11	1.30	1.96	3.64	1.39	1.41	1.02	2.98	3.19	1.33	1.42	4.23	1.90	2.39	1.72	2.20	2.70	2.50	2.32	8.94	1.80	8.24	1.56
75%	2.87	3.11	1.63	2.22	2.81	1.57	1.20	1.35	1.09	1.42	1.08	1.09	1.07	6.10	1.44	2.09	5.64	1.58	1.66	1.06	3.56	3.63	1.45	1.54	5.22	2.04	2.86	2.25	4.26	4.35	3.33	2.68	12.06	2.78	18.85	1.63
max	3.76	3.55	1.82	2.52	3.22	1.79	1.37	1.51	1.12	1.79	1.16	1.11	1.56	8.02	1.69	2.55	7.51	1.97	1.91	1.42	4.53	4.54	1.91	1.80	7.41	2.52	3.55	4.61	8.06	12.50	4.26	3.46	17.73	4.31	40.38	2.55

	variable	coef_abs_value
5	real_age	0.763152
17	roof_Garden	0.816702
29	stadium	0.924263
23	schools	1.019204
22	parks	1.133134
35	bus	1.194681
10	furniture	1.577192
31	museum	1.961709
12	first_floor	2.108294
32	airport	2.118544
28	mall	2.512093
34	subway	2.547014
8	air_conditioning	2.706255
20	high_impact/area	3.583794
4	is_house	3.756806
0	bedrooms	3.815501
33	industry	4.076728
1	bathrooms	11.179291
2	half_bathrooms	11.786271
3	lot_size	25.156524